Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.

An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of
LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for
DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage
and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity
characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented.

/pdf/airborne-lidar-for-dem-generation-some-critical-issues-sxb3b6yug9.pdf

Airborne LiDAR for DEM generation: some critical issues

This paper reviews LiDAR ground filtering algorithms used in the process of creating Digital Elevation Models. We discuss critical issues for the development and application of LiDAR ground filtering algorithms, including filtering procedures for different feature types, and criteria for study site selection, accuracy assessment, and algorithm classification. This review highlights three feature types for which current ground filtering algorithms are suboptimal, and which can be improved upon in future studies: surfaces with rough terrain or discontinuous slope, dense forest areas that laser beams cannot penetrate, and regions with low vegetation that is often ignored by ground filters.

https://www.mdpi.com/2072-4292/2/3/833/pdf

Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues

This study presents a new method of detecting individual treetops from lidar data and applies marker-controlled watershed segmentation into isolating individual trees in savanna woodland. The treetops were detected by searching local maxima in a canopy maxima model (CMM) with variable window sizes. Different from previous methods, the variable windows sizes were determined by the lower-limit of the prediction intervals of the regression curve between crown size and tree height. The canopy maxima model was created to reduce the commission errors of treetop detection. Treetops were also detected based on the fact that they are typically located around the center of crowns. The tree delineation accuracy was evaluated by a five-fold, cross-validation method. Results showed that the absolute accuracy of tree isolation was 64.1 percent, which was much higher than the accuracy of the method, which only searched local maxima within window sizes determined by the regression curve (37.0 percent).

/pdf/isolating-individual-trees-in-a-savanna-woodland-using-small-51gzlb7eer.pdf

Isolating individual trees in a savanna woodland using small footprint lidar data

The vertical extent of waveforms collected by the Geoscience Laser Altimeter System (onboard ICESat - the Ice, Cloud, and land Elevation Satellite) increases as a function of terrain slope and footprint size (the area on the ground that is illuminated by the laser) Over sloped terrain, returns from both canopy and ground surfaces can occur at the same elevation As a result, the height of the waveform (waveform extent) is insufficient to make estimates of tree height on sloped terrain, and algorithms are needed that are capable of retrieving information about terrain slope from the waveform itself Early work on this problem used a combination of waveform height indices and slope indices from a digital elevation model (DEM) A second generation algorithm was developed using datasets from diverse forests in which forest canopy height has been estimated in the field or by via airborne lidar Forest types considered in this paper include evergreen needleleaf, deciduous broadleaf and mixed stands in temperate North America, and tropical evergreen broadleaf forests in Brazil The algorithm described eliminates the need for a DEM, and estimates forest canopy height with an RMSE of 5 m (83% of variance in forest canopy height explained)

https://www.fs.fed.us/global/iitf/pubs/ja_iitf_2007_lefsky001.pdf

Revised method for forest canopy height estimation from Geoscience Laser Altimeter System waveforms

Filtering methods based on morphological operations have been developed in some previous studies. The biggest challenge for these methods is how to keep the terrain features unchanged while using large window sizes for the morphological opening. Zhang et al. (2003) tried to achieve this goal, but their method required the assumption that the slope is constant. This paper presents a new method to achieve this goal without such restrictions, and methods for filling missing data and removing outliers are proposed. The experimental test results using the ISPRS Commission III/WG3 dataset show that this method performs well for most sites, except those with missing data due to the lack of overlap between swaths. This method also shows encouraging results for laser data with low pulse density.

/pdf/filtering-airborne-laser-scanning-data-with-morphological-3k3qvag82z.pdf

Gengxin Xie

Papers

Filtering airborne laser scanning data with morphological methods